Knowledge Graph and Semantic Computing Knowledge Computing and Language Understanding

2020-01-03

Muppalla, R., Lalithsena, S., Banerjee, T., Sheth, A.: A knowledge graph framework for detecting traffic events using stationary cameras. In: Proceedings of WebSci 2017, pp. 431–436 (2017). https://doi.org/10.1145/3091478.3162384 ...

Author: Xiaoyan Zhu

Publisher: Springer Nature

ISBN: 9789811519567

Category: Computers

Page: 211

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This book constitutes the refereed proceedings of the 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019, held in Hangzhou, China, in August 2019. The 18 revised full papers presented were carefully reviewed and selected from 140 submissions. The papers cover wide research fields including the knowledge graph, the semantic Web, linked data, NLP, information extraction, knowledge representation and reasoning.
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Knowledge Graph and Semantic Computing Knowledge Graph and Cognitive Intelligence

2021-05-05

Topological relationships among items, as well as historical interaction information between items and users can all be stored in knowledge graphs, and these relationships and information is utilized as prior knowledge to promote the ...

Author: Huajun Chen

Publisher: Springer Nature

ISBN: 9789811619649

Category: Computers

Page: 336

View: 108

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This book constitutes the refereed proceedings of the 5th China Conference on Knowledge Graph and Semantic Computing, CCKS 2020, held in Nanchang, China, in November 2020. The 26 revised full papers presented were carefully reviewed and selected from 173 submissions. The papers are organized in topical sections on ​knowledge extraction: lexical and entity; knowledge extraction: relation; knowledge extraction: event; knowledge applications: question answering, dialogue, decision support, and recommendation.
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Knowledge Graph and Semantic Computing Semantic Knowledge and Linked Big Data

2016-11-21

Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2181–2187 (2015) 7. Guo, S., Wang, Q., ...

Author: Huajun Chen

Publisher: Springer

ISBN: 9789811031687

Category: Computers

Page: 250

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This book constitutes the refereed proceedings of the first China Conference on Knowledge Graph and Semantic Computing, CCKS, held in Beijing, China, in September 2016. The 19 revised full papers presented together with 6 shared tasks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on knowledge representation and learning; knowledge graph construction and information extraction; linked data and knowledge-based systems; shared tasks.
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Designing and Building Enterprise Knowledge Graphs

2021-08-05

Please understandthat this is an opinionated book,based on our own experience indesigning and building knowledge graph systems and helping others do the same.Ora was a co-author of the original W3C RDF specificationfrom1997 ...

Author: Juan Sequeda

Publisher: Morgan & Claypool Publishers

ISBN: 9781636391755

Category: Computers

Page: 168

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This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice. It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases. How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
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Graph Based Representation and Reasoning

2021-10-19

Knowledge. Graphs. Mehwish Alam1,2 and Sebastian Rudolph3 1 2 FIZ Karlsruhe – Leibniz Institute for Information ... Since the beginning of the 2000s, Knowledge Graphs have been widely used for modeling various domains ranging from ...

Author: Tanya Braun

Publisher: Springer Nature

ISBN: 9783030869823

Category: Computers

Page: 217

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This book constitutes the proceedings of the 26th International Conference on Conceptual Structures, ICCS 2021, held virtually in September 2021. The 12 full papers and 4 short papers presented were carefully reviewed and selected from 25 submissions. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The papers are organized in the following topical sections: applications of conceptual structures; theory on conceptual structures, and mining conceptual structures.
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Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation EITRT 2021

2022-02-18

control, In this way, the existing problems of unclear risk points and unclear knowledge acquisition can be solved [10]. Based on the concept of ontology, a construction method of knowledge graph of railway safety accidents is proposed ...

Author: Jianying Liang

Publisher: Springer Nature

ISBN: 9789811699092

Category: Technology & Engineering

Page: 832

View: 610

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This book reflects the latest research trends, methods, and experimental results in the field of electrical and information technologies for rail transportation, which covers abundant state-of-the-art research theories and ideas. As a vital field of research that is highly relevant to current developments in a number of technological domains, the subjects it covered include intelligent computing, information processing, communication technology, automatic control, etc. The objective of the proceedings is to provide a major interdisciplinary forum for researchers, engineers, academicians, and industrial professionals to present the most innovative research and development in the field of rail transportation electrical and information technologies. Engineers and researchers in academia, industry, and government will also explore an insightful view of the solutions that combine ideas from multiple disciplines in this field. The volumes serve as an excellent reference work for researchers and graduate students working on rail transportation and electrical and information technologies.
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Data Science

2018-09-10

2.1 Spatial-Temporal Knowledge Graph The concept of knowledge graph was proposed by Google in 2012. Knowledge graph are intended to describe the various entities or concepts that exist in the real world, and also describe the ...

Author: Qinglei Zhou

Publisher: Springer

ISBN: 9789811322068

Category: Computers

Page: 649

View: 147

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This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.
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Deep Learning on Graphs

2021-09-23

Formally, a knowledge graph G = (V, E, R) consists a set of nodes V, a set of relational edges E, and a set of relations R. The nodes are various types of entities and attributes, and the edges include different types of relations ...

Author: Yao Ma

Publisher: Cambridge University Press

ISBN: 9781108934824

Category: Computers

Page:

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Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
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Principles of Big Graph In depth Insight

2023-02-01

A knowledge graph is a preeminent type of semantic network to depict highly complex and big data. Initially, Google used knowledge graphs on an industry level. However, in the subsequent years, Amazon, Microsoft, Wikidata, DBpedia, ...

Author:

Publisher: Elsevier

ISBN: 9780323898119

Category: Computers

Page: 460

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Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers
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MDATA A New Knowledge Representation Model

2021-03-06

In [8], it proposes an improved string comparison algorithm for ontology alignment of the knowledge graph. ... and it is not suitable for describing multi-knowledge graphs with large differences, such as cross-language knowledge graphs.

Author: Yan Jia

Publisher: Springer Nature

ISBN: 9783030715908

Category: Computers

Page: 255

View: 927

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Knowledge representation is an important task in understanding how humans think and learn. Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way. This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis). By modifying the representation of entities and relations in knowledge graphs, dynamic knowledge can be efficiently described with temporal and spatial characteristics. The MDATA model can be regarded as a high-level temporal and spatial knowledge graph model, which has strong capabilities for knowledge representation. This book introduces some key technologies in the MDATA model, such as entity recognition, relation extraction, entity alignment, and knowledge reasoning with spatiotemporal factors. The MDATA model can be applied in many critical applications and this book introduces some typical examples, such as network attack detection, social network analysis, and epidemic assessment. The MDATA model should be of interest to readers from many research fields, such as database, cyberspace security, and social network, as the need for the knowledge representation arises naturally in many practical scenarios.
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